Alzheimer's disease (AD), the most common type of dementia, is a devastating disease with no available treatment or prevention. The current lifetime risk for a 65-year-old individual is estimated to be around 10%. Given current projections, over 13.5 million individuals will be diagnosed with Alzheimer's disease by 2050. As the development of therapeutic strategies progresses through drug trials, the current view is that the optimal window of opportunity to benefit an individual is years before symptoms appear, in what is called the preclinical phase. Preclinical AD is thought to begin with the abnormal accumulation of amyloid plaques in the brain. In research and clinical trials, identifying individuals with these plaques is possible with PET scans and cerebrospinal fluid (CSF) measures. Yet, with a cost of more than $5000 per scan, PiB-PET is an expensive means of screening, and involves exposure to ionizing radiation, a considerable health risk. A CSF test necessitates an invasive lumbar puncture with the accompanying risks of headaches, back pain, bleeding, infection and even potential brainstem herniation. We propose to develop software tools that make use of structural MRI scan data to predict the amyloid status of cognitively normal individuals undergoing a screening process, and, for a set of individuals, identify those most at risk of progressing to AD. We believe a two-step MRI + PiB-PET screening procedure will yield significant cost savings by reducing the number of expensive PET scans, and might further help limit redundant radiation exposure due to unnecessary PET imaging in low-risk individuals. Lastly, the identification of individuals with amyloid accumulation who are at risk of imminent clinical decline will be invaluable for drug trial recruitment and future clinical management.